Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

House prices grew year-on-year in most states in the U.S. in the third quarter of 2024. The District of Columbia was the only exception, with a decline of ***** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded ** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2024.

Real Estate Sales 730 Days

City of Hartford real estate sales for the past 2 years

By [source]

About this dataset

This dataset contains data on City of Hartford real estate sales for the last two years, with comprehensive records including property ID, parcel ID, sale date, sale price and more. This dataset is continuously updated each night and sourced from an official reliable source. The columns in this dataset include LocationStartNumber, ApartmentUnitNumber, StreetNameAndWay, LandSF TotalFinishedArea, LivingUnits ,OwnerLastName OwnerFirstName ,PrimaryGrantor ,SaleDate SalePrice ,TotalAppraisedValue and LegalReference - all valuable information to anyone wishing to understand the recent market trends and developments in the City of Hartford real estate industry. With this data providing detailed insights into what properties are selling at what time frame and for how much money – let’s see what secrets we can learn from examining the City of Hartford real estate activity!

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For more datasets, click here.

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How to use the dataset

This dataset contains helpful information about homes sold in the Hartford area over the past two years. This data can be used to analyze trends in real estate markets, as well as monitor sales activity for various areas.

In order to use this dataset, you will need knowledge of EDA (Exploratory Data Analysis) such as data cleaning and data visualization techniques. You will also need a basic understanding of SQL queries and Python scripting language.

The first step is to familiarize yourself with the columns and information contained within the dataset by analyzing descriptive statistics like mean, min, max etc. Next you can filter or “slice” the data based on certain criteria or variables that interest you - such as sale date range, location (by street name or zip code), sale price range, type of dwelling unit etc. After using various filters for analysis it is important to take an error-check step by looking for outliers or any discrepancies that may exist - this will ensure more accuracy in results when plotting graphs and visualizing trends via software tools like Tableau and Power BI etc.

Next you can conduct exploratory analysis through plot visualizations of relationships between buyer characteristics (first & last name) vs prices over time; living units vs square footage stats; average price per bedroom/bathroom ratio comparisons etc – all while taking into account external factors such as seasonal changeovers that could affect pricing fluctuations during given intervals across multiple neighborhoods - use interactive maps if available ets. At this point it's easy to compile insightful reports containing commonalities amongst buyers and begin generalizing your findings with extrapolations which allow us gain a better understanding of current market conditions across different demographic spectrums being compared ie traditional Vs luxury properties – all made possible simply through dedicated research with datasets like these!

Research Ideas

• Analyzing market trends in the City of Hartford's real estate industry by tracking sale prices and appraised values over time to identify regions who are being under or over valued.
• Conducting a predictive analysis project to predict future sales prices, annual appreciation rates, and key features associated with residential properties such as total finished area and living units for investment purposes.
• Studying the impact of local zoning laws on property ownership and development by comparing sale dates, primary grantors, legal references, street names and ways in a given area over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: real-estate-sales-730-days-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------| | LocationStartNumber | The starting number of the location of the property. (Integer) | | ApartmentUnitNumber | The apartment unit number of the property. (Integer) | | StreetNameAndWay | The st...

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to May 2025 about active listing, listing, and USA.

The average home in the U.S. sold for several percent below its asking price in December 2022, as a result of the housing market slowing. Just a few months before that, In the second quarter of 2022, the so-called sale-to-list price ratio went above 100. This reflected the high housing demand and the need of prospective home buyers to bid above the asking price. Housing demand - as measured in pending home sales - went up, as mortgage rates were historically low and plummeted once rates were increased.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

These datasets contain comprehensive information on current real estate listings in Washington, D.C., obtained from Zillow, and offer a detailed overview of the Washington, D.C. housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The full dataset includes all details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With over 5,000 current listings, this dataset is perfect for in-depth analysis of the Washington, D.C. housing market and the Washington, D.C. real estate scene. Potential applications include:

• Comparing listing prices and price per square foot across various neighborhoods and property types
• Mapping listings to visualize the spatial distribution of available inventory
• Analyzing the age of available housing stock using year-of-construction data
• Assessing typical HOA fees and property taxes for listings
• Identifying listings with desirable amenities
• Evaluating school quality near listings using GreatSchools ratings
• Contacting listing agents programmatically using the provided agent information

Whether you're a real estate professional, market analyst, data scientist, or simply interested in the Washington, D.C., housing market, this dataset offers a wealth of information to explore. You can begin investigating and discovering insights into Washington, D.C. real estate today.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

DESCRIPTION
Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE
Zillow: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. Zillow defines all homes as single-family residential, condominium and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of the CPI itself.

Graph and download economic data for Housing Inventory: Active Listing Count in Florida (ACTLISCOUFL) from Jul 2016 to May 2025 about active listing, FL, listing, and USA.

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

DESCRIPTION
Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE
Zillow: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. Zillow defines all homes as single-family residential, condominium and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of the CPI itself.

Graph and download economic data for Housing Inventory: Active Listing Count in Chicago-Naperville-Elgin, IL-IN-WI (CBSA) (ACTLISCOU16980) from Jul 2016 to May 2025 about Chicago, IL, active listing, IN, WI, listing, and USA.

A global database of Real Estate Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date urban planning data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial real estate dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Urban Planning Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Urban Planning Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our location data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Real Estate databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Graph and download economic data for All-Transactions House Price Index for San Diego-Chula Vista-Carlsbad, CA (MSA) (ATNHPIUS41740Q) from Q4 1975 to Q1 2025 about San Diego, appraisers, CA, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for All-Transactions House Price Index for Michigan (MISTHPI) from Q1 1975 to Q1 2025 about MI, appraisers, HPI, housing, price index, indexes, price, and USA.

About Dataset

Dataset Overview This dataset provides a snapshot of real estate transactions in London for 2024. It includes key property details such as the number of bedrooms, bathrooms, living space size, lot size, and transaction price. Additionally, it incorporates information about property features like waterfront views, renovation history, and construction quality. Designed for educational and research purposes, the dataset offers insights into London's real estate market trends and serves as a valuable resource for data analysis and machine learning applications.

Data Science Applications This dataset is ideal for students, researchers, and professionals seeking to apply data science techniques to real-world real estate data. Potential applications include:

Exploratory Data Analysis (EDA): Investigate price trends, property characteristics, and geographical distribution of transactions. Price Prediction Models: Develop machine learning models to predict property prices based on features like size, location, and condition. Trend Analysis: Analyze historical and geographical trends in property prices and features. Geospatial Analysis: Map properties based on latitude and longitude to identify hotspots or underserved areas.

Column Descriptions

Ethically Mined Data This dataset was ethically sourced from publicly available property listings. It does not include any Personally Identifiable Information (PII) or data that could infringe on individual privacy. All information represents public details about properties for sale in London.

Acknowledgements

Data Source: Real estate data provided from publicly accessible resources. Image Credit: Unsplash for real estate-themed visuals. Use this dataset responsibly for educational and analytical purposes!

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

In the first quarter of 2025, San Francisco, Chicago, New York, and Honolulu were some of the U.S. cities with the highest housing construction costs. Meanwhile, Phoenix had one of the lowest construction costs for high-end multifamily homes at *** U.S. dollars per square foot and Las Vegas for single-family homes between *** and *** U.S. dollars per square foot. Construction cost disparities As seen here, the construction cost for a high-end multi-family home in San Francisco in the first quarter of 2024 was over ***** more expensive than in Phoenix. Meanwhile, there were also great differences in the cost of building a single-family house in New York and in Portland or Seattle. Some factors that may cause these disparities are the construction materials, installation, and composite costs, differing land values, wages, etc. For example, although the price of construction materials in the U.S. was rising at a slower level than in 2022 and 2023, several materials that are essential in most construction projects had growth rates of over **** percent in 2024. Growing industry revenue Despite the economic uncertainty and other challenges, the size of the private construction market in the U.S. rose during the past years. It is important to consider that supply and demand for housing influences the revenue of this segment of the construction market. On the supply side, single-family home construction fell in 2023, but it is expected to rise in 2024 and 2025. On the demand side, some of the U.S. metropolitan areas with the highest sale prices of single-family homes were located in California, with San Jose-Sunnyvale-Santa Clara at the top of the ranking.